Simulated gene expression profiles dataset.

Description

Simulated gene expression time series for 55 genes and 30 timepoints generated as in Lebre et al (2010). In the simulation model, every gene may be target gene but only genes 51 to 55 are parent genes.

Usage

data(simulatedProfiles)

Format

A matrix of 55 row (genes) by 30 columns (timepoints).

References

Statistical inference of the time-varying structure of gene-regulation networks S. Lebre, J. Becq, F. Devaux, M. P. H. Stumpf, G. Lelandais, BMC Systems Biology, 2010, 4:130.

Examples

# Load the ARTIVA R package
library(ARTIVA)

# Load the simulated profiles dataset
data(simulatedProfiles)

# Name of the target gene to be analyzed with ARTIVA 
targetGene = 1

# Names of the parent genes (typically transcription factors) 
parentGenes = c("TF1", "TF2", "TF3", "TF4", "TF5")

# Plot of the gene expression profiles for target gene and parent genes
traceGeneProfiles(targetData= simulatedProfiles[targetGene,], 
		  parentData= simulatedProfiles[parentGenes,])

###
# ARTIVA analysis searching for potential interactions between the target 
# genes and a predefined list of parent genes. 
###

# Note that the number of iterations in the RJ-MCMC sampling is reduced 
# to in this example to 'niter=20000' in order obtain a quick overview of
# the ARTIVAnet fonction, but it should be increased (e.g. up to 50000)
# for a better parameter estimation.
## Not run: 
ARTIVAtest = ARTIVAsubnet(targetData = simulatedProfiles[targetGene,],
  parentData = simulatedProfiles[parentGenes,],
  targetName = targetGene,
  parentNames = parentGenes,
  niter = 5000,
  savePictures = FALSE)

## End(Not run)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(ARTIVA)
Loading required package: MASS
Loading required package: igraph

Attaching package: 'igraph'

The following objects are masked from 'package:stats':

    decompose, spectrum

The following object is masked from 'package:base':

    union

Loading required package: gplots

Attaching package: 'gplots'

The following object is masked from 'package:stats':

    lowess

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/ARTIVA/simulatedProfiles.Rd_%03d_medium.png", width=480, height=480)
> ### Name: simulatedProfiles
> ### Title: Simulated gene expression profiles dataset.
> ### Aliases: simulatedProfiles
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
> # Load the ARTIVA R package
> library(ARTIVA)
> 
> # Load the simulated profiles dataset
> data(simulatedProfiles)
> 
> # Name of the target gene to be analyzed with ARTIVA 
> targetGene = 1
> 
> # Names of the parent genes (typically transcription factors) 
> parentGenes = c("TF1", "TF2", "TF3", "TF4", "TF5")
> 
> # Plot of the gene expression profiles for target gene and parent genes
> traceGeneProfiles(targetData= simulatedProfiles[targetGene,], 
+ 		  parentData= simulatedProfiles[parentGenes,])
> 
> ###
> # ARTIVA analysis searching for potential interactions between the target 
> # genes and a predefined list of parent genes. 
> ###
> 
> # Note that the number of iterations in the RJ-MCMC sampling is reduced 
> # to in this example to 'niter=20000' in order obtain a quick overview of
> # the ARTIVAnet fonction, but it should be increased (e.g. up to 50000)
> # for a better parameter estimation.
> ## Not run: 
> ##D ARTIVAtest = ARTIVAsubnet(targetData = simulatedProfiles[targetGene,],
> ##D   parentData = simulatedProfiles[parentGenes,],
> ##D   targetName = targetGene,
> ##D   parentNames = parentGenes,
> ##D   niter = 5000,
> ##D   savePictures = FALSE)
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>